I saw the emphasis on this site and was a bit intrigued. As of late, I have been thinking of writing off back testing as something that is not required for my method of trading as I am about as a discretionary trader as one can get – I use support and resistance and a few other price points to influence my trading, but overall, it is just about taking “promising” set-ups I am familiar with. I am beginner so my question is am I missing something, i.e. should I still not give up on backtesting because it could be of some intrinsic value? Thank you much, John
This is a very excellent question. First, if you are getting the results you want then obviously it is not required. However, utilizing backtesting, quantitative studies, algorithmic edge, and simulations can elucidate specific, unique, non-obvious insight into markets generally referred to as edge. The greatest difficulty for integrating these sorts of processes as an active process for the day trader is that many forms of day trading are facilitated best when the trader is in a well-rested open minded and peak performance state. A typical process might be for the day trader to observe certain patterns of interest or ideas during the morning or trading day and then run studies at night. However, if one doesn’t have the resources for a dedicated researcher then it can create a time crunch and fatigue and exhaustion can easily set in. On the other hand, if you don’t test your best insights and observations then you give up many opportunities for developing edge. One possible solution to the fatigue/performance split might be to avoid being too reactive in your trading. This might be accomplished by trading on the simulator and developing your insights more slowly.
Many think of the term “backtesting” as some a singular process that results in a system but it can refer to different processes. Below, I suggest some of the benefits and limitations of the processes,
Curtis is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline.
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